code: def load_adm_sat_school_data(return_X_y=False):
      with open("./merged_adm_sat_data.csv") as csv_file:
          data_file = csv.reader(csv_file)
          temp = next(data_file)
          n_samples = int(temp[0])
          n_features = int(temp[1])
          target_names = np.array(temp[2:])
  
      df = pd.read_csv(
          "./merged_adm_sat_data.csv",
          sep=",",
          usecols=(0, 1, 2, 3),
          skiprows=0,
      )
      data = np.empty((n_samples, n_features), dtype=int)
      target = np.ma.empty((n_samples,), dtype=int)
  
      for index, row in df.iterrows():
          data[index] = np.asarray(
              [df.iloc[index][0], df.iloc[index][1], df.iloc[index][2]],
              dtype=np.float,
          )
          target[index] = np.asarray(df.iloc[index][3], dtype=np.int)
  
      feature_names = np.array(
          ["ACT_AVG", "SAT_AVG", "GRAD_DEBT", "REGION"]
      )
  
      if return_X_y:
          return data, target
  
      return datasets.base.Bunch(
          data=data,
          target=target,
          target_names=target_names,
          DESCR="School Data set",
          feature_names=feature_names,
      )
  
file_path: yellowbrick/examples/balavenkatesan/testing.py
imports: import pandas
  import csv
  from sklearn import neighbors
  from sklearn import datasets
  import numpy
  import yellowbrick
  from yellowbrick.neighbors import KnnDecisionBoundariesVisualizer
  import matplotlib.pyplot
  from matplotlib.colors import ListedColormap
label_desc: Use image node_id 1 for calling a global function with example usage: load_adm_sat_school_data(return_X_y) and returns: datasets, data,  target